It is essential to detect failure of parts of vehicles, particularly, machines during early stages so as to avoid damage of other parts of the vehicles. In certain scenarios, faults are detected late, and in certain other scenarios faults are not detected at all. In such scenarios, the faults may lead to a cascading effect leading to complete failure of the machinery, which can be hazardous.
One example of such machinery includes a vehicle. With the growth of automotive industry, it has been observed that the vehicles are being equipped with numerous sensitive and complex components, since the automobile manufacturers are seeking to improve the quality of automotive products. Auto Original Equipment Manufacturers' (OEM) are struggling to identify emerging failures of vehicle parts that could cascade and potentially lead to recalls. Vehicle field failure early warning relates to the ability to predict emerging failures so that the auto OEM can take precautionary measures and avoid a potential recall.
Conventionally, historical failure data of the vehicles is used to model failure rate of vehicle parts. The inventors here have recognized several technical problems with such conventional systems, as explained below. The detection of vehicle parts failures using the conventional methods has its limitations, since the historical failure data such as warranty claims, is a lagging indicator of failure and does not also capture problems faced by the customer. This eventually leads to a longer emerging failure detection cycle.